AI Analysis
The package exhibits high network and shell execution risks, suggesting potential unauthorized actions. However, lack of obfuscation and credential harvesting patterns, along with insufficient evidence of malice, makes it difficult to conclude a supply-chain attack.
- High network risk (7/10)
- High shell risk (8/10)
Per-check LLM notes
- Network: The network calls suggest the package may be performing external communications, possibly unauthorized actions like fetching tokens or other sensitive information.
- Shell: Subprocess execution might indicate the package is invoking external commands, which could potentially be used to perform unauthorized actions on the system.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The repository not being found and the maintainer having only one package suggest potential risks, but there's insufficient evidence to conclusively determine malice.
Package Quality Overall: Low (4.2/10)
Partial test coverage signals detected
1 test file(s) detected (e.g. test_backend.py)
Some documentation present
Detailed PyPI description (9098 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
73 type-annotated function signatures detected in source
Could not retrieve contributor data from GitHub
GitHub API error: 404
Heuristic Checks
Found 3 network call pattern(s)
vice) try: resp = requests.get(clean_url, allow_redirects=False, timeout=10) except req} try: resp = requests.post( url, json={ "scope"try: resp = requests.post( url, data={
No obfuscation patterns detected
Found 3 shell execution pattern(s)
try: result = subprocess.run( [str(_HELPER_PATH), "get-access-token"],""" try: result = subprocess.run( [_AZ_EXE, "account", "show", "--query", "user.ntry: result = subprocess.run( [ _AZ_EXE,
No credential harvesting patterns detected
No typosquatting candidates detected
No author email provided
All external links appear legitimate
Repository not found (deleted or private)
Repository not found (deleted or private)
1 maintainer concern(s) found
Author "msr-central" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a Python-based utility named 'ArtiKeyManager' that simplifies the management of secrets stored in Azure DevOps Artifacts feeds using the 'artifacts-keyring-nofuss' package. This utility should allow users to store, retrieve, and delete secrets securely from their Azure DevOps Artifacts feed without needing to manage complex configurations. Here are the steps and features your application should include: 1. **Setup**: Ensure your application starts by setting up an Azure DevOps Artifacts feed URL and personal access token (PAT) as required inputs. Use the 'artifacts-keyring-nofuss' package to initialize the keyring backend with these credentials. 2. **Store Secrets**: Implement a feature where users can input a secret (e.g., API keys, passwords) along with a label or name to uniquely identify it. The utility should then store this secret in the Azure DevOps Artifacts feed using the 'artifacts-keyring-nofuss' package. 3. **Retrieve Secrets**: Provide functionality for users to retrieve a secret based on its unique identifier or label. The utility should fetch the secret from the feed and display it to the user. 4. **Delete Secrets**: Include a feature allowing users to delete a secret from the feed by providing its identifier or label. Ensure this operation is irreversible within the utility. 5. **Security Measures**: Since handling secrets is sensitive, ensure all interactions with the feed are secure. Use HTTPS and validate the connection to the feed before performing any operations. 6. **User Interface**: Design a simple command-line interface (CLI) for interacting with the utility. Commands should be intuitive and clearly documented. 7. **Error Handling**: Implement robust error handling to manage cases such as invalid inputs, connection failures, or authentication issues. Provide meaningful error messages to guide users. 8. **Documentation**: Alongside the code, create comprehensive documentation explaining how to install and use the utility. Include examples of common use cases. By following these guidelines, you'll develop a practical tool that leverages the 'artifacts-keyring-nofuss' package to make managing secrets in Azure DevOps Artifacts feeds more accessible and secure.
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